Note Basic use-case. If your dataset isn’t split into train/validation/test splits, the simplest dataset structure is to have one file: data.csv

Note You can name your data files after the train, test, and validation splits

Note If you don’t have any non-traditional splits, then you can place the split name anywhere in the data file. The only rule is that the split name must be delimited by non-word characters, like test-file.csv for example instead of testfile.csv. Supported delimiters include underscores, dashes, spaces, dots, and numbers.

Note You can place your data files into different directories named train, test, and validation where each directory contains the data files for that split.

Note There are several ways to refer to train/validation/test splits. Validation splits are sometimes called “dev”, and test splits may be referred to as “eval”. These other split names are also supported, and the following keywords are equivalent: - train, training - validation, valid, val, dev - test, testing, eval, evaluation

Note Splits can span several files. Make sure all the files of your train set have train in their names (same for test and validation). You can even add a prefix or suffix to train in the file name (like my_train_file_00001.csv for example).

Note For convenience, you can also place your data files into different directories. In this case, the split name is inferred from the directory name.

Note If your dataset splits have custom names that aren’t train, test, or validation, then you can name your data files like data/-xxxxx-of-xxxxx.csv.